模型在线验证机制实现

模型在线验证机制实现

核心监控指标

• 模型输出稳定性：监控预测结果的标准差，当超过阈值0.1时触发告警
• 特征分布漂移：使用KS检验监控输入特征分布变化，p值小于0.05时告警
• 模型性能下降：AUC、准确率等指标连续3个周期下降超过5%时告警

实现步骤

1. 创建监控指标收集器

import pandas as pd
from scipy import stats

class ModelValidator:
    def __init__(self):
        self.baseline_performance = {}
        self.feature_history = []
    
    def validate_output_stability(self, predictions):
        std_dev = np.std(predictions)
        if std_dev > 0.1:
            self.send_alert('Output instability detected', std_dev)
            return False
        return True
    
    def validate_feature_drift(self, current_features, baseline_features):
        ks_stat, p_value = stats.ks_2samp(current_features, baseline_features)
        if p_value < 0.05:
            self.send_alert('Feature drift detected', ks_stat)
            return False
        return True

2. 配置告警规则

• 严重级别：输出不稳定、特征漂移
• 通知渠道：邮件+Slack webhook
• 重试机制：连续3次告警后自动触发模型重新训练

告警配置文件

alerts:
  - name: "ModelOutputStability"
    threshold: 0.1
    severity: "critical"
    notification: ["email", "slack"]
  - name: "FeatureDrift"
    threshold: 0.05
    severity: "warning"
    notification: ["slack"]